? ? Studies of quantitative image processing for longitudinal neuroimaging followup have been reported for more than two decades. Many algorithms and methods of brain image processing and analysis have been reported, validated and are freely available, but very few have been engineered for practical clinical and research use or integrated into clinical information systems. This has seriously limited the impact that computational algorithms and methods developed by the medical imaging research community have made on clinical neuroimaging practice and neuroimaging based clinical research. ? ? We propose to integrate available validated neuroimage analysis tools into an image informatics system AFINITI (Assisted Followup in Neuroimaging of Therapeutic Intervention), designed to enhance diagnostic accuracy and reduce error in the interpretation of neuroimaging followup studies for longitudinal followup of therapeutic intervention in several common conditions, by providing the interpreting and referring physicians with automated tools and augmented information needed for precise, reproducible, quantitative assessment of longitudinal change in brain images. The primary goal is to translate tools and techniques produced by biomedical informatics research into an actual clinical setting where it can be used in diagnosis, therapeutic decision making, treatment response monitoring, clinical education and research in neurological disease. ? ? The AFINITI system will be developed, evaluated, and implemented into the clinical workflow and validated in Brigham and Women's Hospital-Harvard Medical School (BWH-HMS) clinical neuroradiology section and the BWH-HMS Longwood MRI Research Center (LMRC). We will package the validated software into a software toolkit and distribute it in an open source environment, compatible with popular medical imaging platforms, such as VTK (Visualization Toolkit), ITK (Insight Toolkit), and 3D-Slicer.The data integration method and database schemas for this neuroimage database will also be made available to public. AFINITI system architecture and components will form a prototype for the development of future clinical imaging followup systems in other imaging centers. This will be a substantial contribution to the public health. ? ?

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Resources Project Grant (NLM) (G08)
Project #
5G08LM008937-02
Application #
7499603
Study Section
Special Emphasis Panel (ZLM1-ZH-H (M3))
Program Officer
Sim, Hua-Chuan
Project Start
2007-09-30
Project End
2010-09-29
Budget Start
2008-09-30
Budget End
2009-09-29
Support Year
2
Fiscal Year
2008
Total Cost
$144,048
Indirect Cost
Name
Methodist Hospital Research Institute
Department
Type
DUNS #
185641052
City
Houston
State
TX
Country
United States
Zip Code
77030
Zhu, Ying; Young, Geoffrey S; Xue, Zhong et al. (2012) Semi-automatic segmentation software for quantitative clinical brain glioblastoma evaluation. Acad Radiol 19:977-85
Hu, Xintao; Wong, Kelvin K; Young, Geoffrey S et al. (2011) Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 33:296-305
Li, Hai; Xue, Zhong; Cui, Kemi et al. (2011) Diffusion tensor-based fast marching for modeling human brain connectivity network. Comput Med Imaging Graph 35:167-78
Li, Hai; Xue, Zhong; Guo, Lei et al. (2010) A hybrid approach to automatic clustering of white matter fibers. Neuroimage 49:1249-58
Xue, Zhong; Wong, Kelvin; Wong, Stephen T C (2010) Joint registration and segmentation of serial lung CT images for image-guided lung cancer diagnosis and therapy. Comput Med Imaging Graph 34:55-60
Xue, Zhong; Li, Hai; Guo, Lei et al. (2010) A local fast marching-based diffusion tensor image registration algorithm by simultaneously considering spatial deformation and tensor orientation. Neuroimage 52:119-30
Nie, Jingxin; Xue, Zhong; Liu, Tianming et al. (2009) Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field. Comput Med Imaging Graph 33:431-41
Xue, Zhong; Shen, Dinggang (2009) A new statistically-constrained deformable registration framework for MR brain images. Int J Med Eng Inform 1:357-367